• Corpus ID: 236171363

Differentially Private Algorithms for 2020 Census Detailed DHC Race \& Ethnicity

@article{Haney2021DifferentiallyPA,
  title={Differentially Private Algorithms for 2020 Census Detailed DHC Race \\& Ethnicity},
  author={Samuel Haney and William Sexton and Ashwin Machanavajjhala and Michael Hay and Gerome Miklau},
  journal={ArXiv},
  year={2021},
  volume={abs/2107.10659}
}
This article describes a proposed differentially private (DP) algorithms that the US Census Bureau is considering to release the Detailed Demographic and Housing Characteristics (DHC) Race & Ethnicity tabulations as part of the 2020 Census. The tabulations contain statistics (counts) of demographic and housing characteristics of the entire population of the US crossed with detailed races and tribes at varying levels of geography. We describe two differentially private algorithmic strategies… 

Tables from this paper

Assessing Statistical Disclosure Risk for Differentially Private, Hierarchical Count Data, with Application to the 2020 U.S. Decennial Census

We propose Bayesian methods to assess the statistical disclosure risk of data released under zero-concentrated differential privacy, focusing on settings with a strong hierarchical structure and

Bit-efficient Numerical Aggregation and Stronger Privacy for Trust in Federated Analytics

This work proposes numerical aggregation protocols that empirically improve upon prior art, while providing compa-rable local differential privacy guarantees and sharing a single private bit per value supports privacy metering that enables privacy controls and guarantees that are not covered by differential privacy.

Answering Private Linear Queries Adaptively using the Common Mechanism

For linear queries, a method for decom-posing M 1 and M 2 into three parts is proposed, which is completely equivalent to running M 1 (both in terms of query answer accuracy and total privacy cost 𝜌 ).

References

SHOWING 1-9 OF 9 REFERENCES

The Discrete Gaussian for Differential Privacy

This work theoretically and experimentally shows that adding discrete Gaussian noise provides essentially the same privacy and accuracy guarantees as the addition of continuousGaussian noise, and presents an simple and efficient algorithm for exact sampling from this distribution.

Concentrated Differential Privacy: Simplifications, Extensions, and Lower Bounds

This work presents an alternative formulation of the concept of concentrated differential privacy in terms of the Renyi divergence between the distributions obtained by running an algorithm on neighboring inputs, which proves sharper quantitative results, establishes lower bounds, and raises a few new questions.

Rényi Differential Privacy

  • Ilya Mironov
  • Computer Science
    2017 IEEE 30th Computer Security Foundations Symposium (CSF)
  • 2017
This work argues that the useful analytical tool can be used as a privacy definition, compactly and accurately representing guarantees on the tails of the privacy loss, and demonstrates that the new definition shares many important properties with the standard definition of differential privacy.

Privacy integrated queries: an extensible platform for privacy-preserving data analysis

PINQ's unconditional structural guarantees require no trust placed in the expertise or diligence of the analysts, substantially broadening the scope for design and deployment of privacy-preserving data analysis, especially by non-experts.

Concentrated differential privacy: Simplifications

  • extensions, and lower bounds. CoRR,
  • 2016

Potential privacy lapse found in americans' 2010 census data

    Potential privacy lapse found in americans

    • 2010 census data
    • 2019

    demonstration data products -design parameters and global privacy-loss budget

      Code Title 13-CENSUS